Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators.** We evaluate MORE ACQUISITIONS PLC prediction models with Modular Neural Network (CNN Layer) and Pearson Correlation ^{1,2,3,4} and conclude that the LON:TMOR stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:TMOR stock.**

**LON:TMOR, MORE ACQUISITIONS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trading Interaction
- What is a prediction confidence?
- Dominated Move

## LON:TMOR Target Price Prediction Modeling Methodology

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. We consider MORE ACQUISITIONS PLC Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:TMOR stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Pearson Correlation)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:TMOR stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## LON:TMOR Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:TMOR MORE ACQUISITIONS PLC

**Time series to forecast n: 22 Sep 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:TMOR stock.**

**X axis: *Likelihood%** (The higher the percentage value, the more likely the event will occur.)

**Y axis: *Potential Impact%** (The higher the percentage value, the more likely the price will deviate.)

**Z axis (Yellow to Green): *Technical Analysis%**

## Conclusions

MORE ACQUISITIONS PLC assigned short-term Ba3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Pearson Correlation ^{1,2,3,4} and conclude that the LON:TMOR stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:TMOR stock.**

### Financial State Forecast for LON:TMOR Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | B2 |

Operational Risk | 54 | 85 |

Market Risk | 85 | 32 |

Technical Analysis | 59 | 65 |

Fundamental Analysis | 84 | 53 |

Risk Unsystematic | 40 | 37 |

### Prediction Confidence Score

## References

- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
- Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97

## Frequently Asked Questions

Q: What is the prediction methodology for LON:TMOR stock?A: LON:TMOR stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Pearson Correlation

Q: Is LON:TMOR stock a buy or sell?

A: The dominant strategy among neural network is to Sell LON:TMOR Stock.

Q: Is MORE ACQUISITIONS PLC stock a good investment?

A: The consensus rating for MORE ACQUISITIONS PLC is Sell and assigned short-term Ba3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of LON:TMOR stock?

A: The consensus rating for LON:TMOR is Sell.

Q: What is the prediction period for LON:TMOR stock?

A: The prediction period for LON:TMOR is (n+1 year)

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